Shortcuts

Source code for flash.core.data.utils

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import os.path
import tarfile
import zipfile
from typing import Any, Callable, Dict, Iterable, Mapping, Optional, Set

import requests
import urllib3
from pytorch_lightning.utilities.apply_func import apply_to_collection
from torch import nn
from tqdm.auto import tqdm as tq

from flash.core.utilities.imports import _CORE_TESTING
from flash.core.utilities.stages import RunningStage

# Skip doctests if requirements aren't available
if not _CORE_TESTING:
    __doctest_skip__ = ["download_data"]

_STAGES_PREFIX = {
    RunningStage.TRAINING: "train",
    RunningStage.TESTING: "test",
    RunningStage.VALIDATING: "val",
    RunningStage.PREDICTING: "predict",
    RunningStage.SERVING: "serve",
    RunningStage.SANITY_CHECKING: "val",
}

_INPUT_TRANSFORM_FUNCS: Set[str] = {
    "per_sample_transform",
    "per_batch_transform",
    "per_sample_transform_on_device",
    "per_batch_transform_on_device",
    "collate",
}

_CALLBACK_FUNCS: Set[str] = {
    "load_sample",
    *_INPUT_TRANSFORM_FUNCS,
}

_OUTPUT_TRANSFORM_FUNCS: Set[str] = {
    "per_batch_transform",
    "uncollate",
    "per_sample_transform",
}


[docs]def download_data(url: str, path: str = "data/", verbose: bool = False) -> None: """Download file with progressbar. # Code adapted from: https://gist.github.com/ruxi/5d6803c116ec1130d484a4ab8c00c603 # __author__ = "github.com/ruxi" # __license__ = "MIT" Examples ________ .. doctest:: >>> import os >>> from flash.core.data.utils import download_data >>> download_data("https://pl-flash-data.s3.amazonaws.com/titanic.zip", "./data") >>> os.listdir("./data") # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE [...] """ # Disable warning about making an insecure request urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning) if not os.path.exists(path): os.makedirs(path) local_filename = os.path.join(path, url.split("/")[-1]) r = requests.get(url, stream=True, verify=False) file_size = int(r.headers["Content-Length"]) if "Content-Length" in r.headers else 0 chunk_size = 1024 num_bars = int(file_size / chunk_size) if verbose: print(dict(file_size=file_size)) print(dict(num_bars=num_bars)) if not os.path.exists(local_filename): with open(local_filename, "wb") as fp: for chunk in tq( r.iter_content(chunk_size=chunk_size), total=num_bars, unit="KB", desc=local_filename, leave=True, # progressbar stays ): fp.write(chunk) # type: ignore def extract_tarfile(file_path: str, extract_path: str, mode: str): if os.path.exists(file_path): with tarfile.open(file_path, mode=mode) as tar_ref: for member in tar_ref.getmembers(): try: tar_ref.extract(member, path=extract_path, set_attrs=False) except PermissionError: raise PermissionError(f"Could not extract tar file {file_path}") if ".zip" in local_filename: if os.path.exists(local_filename): with zipfile.ZipFile(local_filename, "r") as zip_ref: zip_ref.extractall(path) elif local_filename.endswith(".tar.gz") or local_filename.endswith(".tgz"): extract_tarfile(local_filename, path, "r:gz") elif local_filename.endswith(".tar.bz2") or local_filename.endswith(".tbz"): extract_tarfile(local_filename, path, "r:bz2")
[docs]class FuncModule(nn.Module): """This class is used to wrap a callable within a nn.Module and apply the wrapped function in `__call__`""" def __init__(self, func: Callable) -> None: super().__init__() self.func = func def forward(self, *args, **kwargs) -> Any: return self.func(*args, **kwargs) def __str__(self) -> str: return f"{self.__class__.__name__}({self.func.__name__})" def __repr__(self): return str(self.func)
[docs]def convert_to_modules(transforms: Optional[Dict[str, Callable]]): if transforms is None or isinstance(transforms, nn.Module): return transforms transforms = apply_to_collection(transforms, Callable, FuncModule, wrong_dtype=nn.Module) transforms = apply_to_collection(transforms, Mapping, nn.ModuleDict, wrong_dtype=nn.ModuleDict) transforms = apply_to_collection(transforms, Iterable, nn.ModuleList, wrong_dtype=(nn.ModuleList, nn.ModuleDict)) return transforms

© Copyright 2020-2021, PyTorch Lightning. Revision da42a635.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: stable
Versions
latest
stable
0.8.0
0.7.5
0.7.4
0.7.3
0.7.2
0.7.1
0.7.0
0.6.0
0.5.2
0.5.1
0.5.0
0.4.0
0.3.2
0.3.1
0.3.0
0.2.3
0.2.2
0.2.1
0.2.0
0.1.0post1
Downloads
html
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.